NIR Bands Research Articles

Sentinel-1 and Sentinel-2 data fusion to distinguish building damage level of the 2018 Lombok Earthquake

Following an earthquake, Building Damage Assessment (BDA) is crucial in detecting areas that need immediate rescue actions and planning for the best evacuation strategy. Remote sensing has been widely used for BDA. The availability of Sentinel-1 and Sentinel-2 images that are freely accessible could enhance remote sensing applications for building damage classification caused by natural disasters. The accuracy of using the two satellite images for BDA mapping in tropical regions is still uncertain. This research aims to assess Sentinel-1 and Sentinel-2 data fusion performance for BDA mapping following the catastrophic 2018 Lombok Earthquake. Pre- and post-earthquake images of Sentinel-1 and Sentinel-2 were selected, preprocessed, and integrated using the random forest classifier. Three data scenarios were used in this study, i.e., Sentinel-1, Sentinel-2, and the fusion of both datasets. The results demonstrated that all models showed excellent performance in classifying destroyed buildings compared to severely and moderately damaged buildings. This is due to the medium spatial resolution of the images could not identify the damage level in detail. Sentinel-1 and Sentinel-2 data fusion provided the highest overall accuracy value (62.4%) compared to other single dataset models. According to the feature importance analysis result, GLCM Mean, GLCM Variance, and NIR Band played crucial roles in the damage classification. In conclusion, the fusion of Sentinel-1 and Sentinel-2 can provide the best model for destroyed building mapping. However, severely and moderately building damaged mapping can only be accurately mapped using higher spatial resolution images.

Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery

Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

Bathymetric and Capacity Relationships Based on Sentinel-3 Mission Data for Aswan High Dam Lake, Egypt

Aswan High Dam Lake (AHDL) is one of the most relevant hot spots at both local and global levels after construction of the Grand Ethiopian Renaissance Dam (GERD) was announced. The management of AHDL is a vital task, which requires the input of reliable information such as the lake bathymetry, water level, and the water surface area. Traditional, bathymetric methods are still very expensive and difficult to operate. Nowadays, satellite data and remote sensing techniques are easily accessible. In particular, datasets produced by operational missions are freely and globally available, and may provide efficient and inexpensive solutions for the retrieval of quantitative parameters concerning strategic water bodies, such as AHDL. This work identifies the performance of Sentinel-3A optical imagery data in the visible and NIR bands from the two optical instruments SLSTR and OLCI, and proposes the integration with Sentinel-3A radar altimetry from SRAL instrument applied to AHDL. This preliminary and first study investigated the relationship between the reflectance data and in situ data for water depth after a bathymetric campaign in the deep-water region using statistical regression models. These statistical models showed promising results in terms of correlation value (R2 > 0.8) and normalized root mean square errors (NRMSE < 0.4). Also, Heron’s formula was applied to combine optical imagery and Sentinel-3 altimetry water level datasets to estimate water storage variations in AHDL. In addition, equations governing the relationship between water level, water surface area, and water volume were analyzed. The work is very useful for all authorities and stakeholders dealing with large water bodies.

Enhanced Landsat surface reflectance prediction considering land cover change by using an ensemble of spectro-temporal and spectro-spatial predictions

The cloud-induced contaminations limit the availability of Earth observation data acquired by Landsat satellites. In the field of cloud-contaminated image prediction and restoration, abrupt land cover change is an important but usually unconsidered issue. Based on the existing Time Series Models and Continuous Change Detection algorithm (TSM-CCD), an enhanced TSM-CCD algorithm (eTSM-CCD) is proposed in this paper to improve Landsat surface reflectance (SR) predictions based on all available Landsat data. The proposed eTSM-CCD method integrates time-series model estimation in the temporal domain and similar pixel replacement in the spatial domain to both address the issue of abrupt land cover changes and improve the prediction accuracy of SR. The proposed method was applied to Landsat time-series data in two study areas in the United States. The results indicated that the proposed method achieved higher overall and band-wise (especially NIR band) prediction accuracies than the original TSM-CCD algorithm. Specifically, the accuracies were improved more significantly for land cover types with distinct phenological characteristics at the peak of the growing season. In addition, compared to classical spatiotemporal prediction algorithms, such as modified neighborhood similar pixel interpolator (MNSPI) and enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the proposed method also yielded higher prediction accuracies and performed much better in predicting abrupt changes in land cover. The eTSM-CCD algorithm proposed in this paper is promising for the prediction and restoration of surface reflectance, especially in the vegetated areas during the peak of the growing season or in areas undergoing abrupt land cover changes.

Optical remote sensing cloud detection based on random forest only using the visible light and near-infrared image bands

ABSTRACT Cloud detection is of great significance for optical remote sensing images. Most existing cloud detection approaches often rely on many thresholds among multiple bands with a wide spectrum range, and normally just can be applied to specific satellite data. Cloud detection is difficult in some sensor data with limited number of available spectral bands. To tackle this challenge, we propose a cloud detection method based on random forest (RFCD) only with the most common RGB and NIR bands. The RFCD normalizes different sensor data by calculating the top-of-atmosphere (TOA) reflectance and combines stable spectral and texture features extracted from the most common bands, which improves accuracy, reduces reliance on multi-band information, and improves the extendibility of the method. Moreover, the RFCD effectively avoids the complicated threshold setting and reduces subjective factors. Few parameters and strong generalization ability of RF further improve the extendibility of RFCD and provide the application possibility in a variety of data. Experimental results show the total validity obtained by 107 Landsat-8 images is 93.46%. The accuracy of the RFCD is higher than the Function of mask method and similar to the SegNet deep learning method, while the RFCD need fewer training samples and hyperparameters which makes it easier to be used. The extended experimental results show RFCD also gets good detection results in Sentinel-2 and GaoFen-1 images. The new RFCD is an accurate cloud detection method with strong extendibility and stability, also avoiding the complicated threshold setting.

EXPLORING NDVI AND NDBI RELATIONSHIP USING LANDSAT 8 OLI/TIRS IN KHANGARH TALUKA, GHOTKI

Built-up is one of the most significant type of land use-cover linked with urbanization. Computing, classifying, and mapping the built-up areas by Landsat image, is on priority demand for municipal and policymakers to investigate urban extension. Thus, increasing population and conversion of agricultural land into urban is a major topic understanding the bond between both types of land use. In the context of this, this study investigates the relationship between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) in Khangarh taluka. Therefore, satellite images of Landsat 8 OLI/TIRS 2014, 2016, 2018, and 2021 were downloaded freely from the USGS-GloVIS Earth Explorer website. The images were processed in the ArcGIS 10.3 environment. NDVI was calculated using the Near-Infrared NIR (band 5), Red (band 4) and for NDBI, Middle Infrared Reflectance MIR (band 6) and NIR (band 5) was used following the equation of both indices. The calculated values were then exported in SPSS software for correlation determination and scatter plot development. The results from the case showed that there was a linear and negative correlation between vegetation index and built-up index in all years over the study area. Furthermore, in 2014 the coefficient of correlation explicated R2=0.96; in 2016 R2=0.23, in 2018 R2=0.34, and in 2021 R2=0.22 which indicated that NDBI could be used to illustrate the evaluation of urban construction land. The all-over study recommends that built-up index NDBI not only can be used as a significant indicator for built-up or urban areas estimation but also deliver a consistent source for urban development and planning.

A Knowledge Optimization-Driven Network With Normalizer-Free Group ResNet Prior for Remote Sensing Image Pan-Sharpening

Multispectral images play a crucial role in environmental monitoring or ecological analysis for their large scope, quick acquisition, and big data. With the rapid development of technology and increasing demand, very high-resolution multispectral images have attracted a lot of attention these days. However, due to sensor equipment and the imaging environment, the spatial resolution of multispectral images is always restricted. With the help of panchromatic images, pan-sharpening is a very important technique to enhance the spatial details of multispectral images. In this study, we proposed a knowledge optimization-driven pan-sharpening network with normalizer-free group ResNet prior, called PNXnet, which is unfolded from a physical knowledge optimization-driven variational model. We solved the memory overhead brought by the traditional ResNet relying on batch normalization. Results on four sensors show that high quantitative indexes and natural visual effects have verified the reliability of PNXnet. Focusing on the NIR band where spatial details are hard to be injected, we compared the Normalized Difference Vegetation Index (NDVI) generated from the fused results, the estimated NDVI shows a high consistency to the ground truth with R² above 0.91. Besides, we also compared the model generation. Furthermore, low model complexity and quicker computational speed make the daily application of PNXnet possible.

NDT model study of crown pear based on near infrared spectroscopy

With the growing area and production of pear, as well as the people for the fruit quality requirements are growing. The traditional methods for pear quality inspection are expensive and less accurate, and cannot be done for every pear. In response to the current production environment and market demand, a rapid and non-destructive method of pear quality testing is explored, using near infrared spectroscopy to establish the quality prediction model of crown pear. The main quality indicators for crown pears are soluble solids (SSC) and hardness, using NIR spectroscopy to establish a hardness prediction model and NIR spectroscopy to establish a soluble solids prediction model to build a spectral measurement system. The method was to measure pear spectral images in the NIR band (400nm to 1050nm). The spectra were pre-processed with multiple scattering correction (MSC) and standard normal variables exchange (SNV) to eliminate the influence of extraneous factors, and then downscaled by continuous projection algorithm (SPA) and principal component analysis (PCA) to extract the number of principal factors respectively. Partial Least Squares (PLS) was used to build the regression model and to predict the soluble solids (SSC) and hardness. The best model developed was a hardness prediction model by SNV + principal component analysis with a correlation coefficient of 0.64116 and a soluble solids prediction model by SNV + principal component analysis with a correlation coefficient of 0.73566.

Optical Detection of Marine Debris Using Deep Knockoff

This article investigates the use of deep knockoff, a modern statistical variable selection methodology, to uncover the spectral signatures of marine debris. This method uses a generative model by leveraging deep neural networks to learn the high dimensional distribution of reflectance in visible to near infrared wavelengths. To that end, a public dataset obtained from ground-based labeling of the observations by the Multispectral Instrument (MSI) on-board Sentinel-2 satellite is used. Through controlling the false discovery rate, consistent with the known physical causalities, the results indicate that the near infrared (band 8, 833 nm) and red (band 4, 665 nm) are the most important bands, respectively for discrimination of marine (plastic) debris from the background water. In the presence of dense <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sargassum</i> macroalgae, the deep knockoff isolates the green (band 3, 560 nm) and the narrow NIR (band 8a, 864.7 nm) as another important band.

BOST: A Canopy Reflectance Model Suitable for Both Continuous and Discontinuous Canopies Over Sloping Terrains

Canopy reflectance models over sloping terrain are critical and reliable tools for vegetation biophysical parameter retrieval. However, the applicability of existing models is strictly limited by individual canopy structure, such as continuous or discontinuous canopies, and hybrid vegetation structure is seldom considered in current models. Therefore, this defect greatly limits the application of canopy reflectance models in rugged terrains. To overcome this problem, we developed a canopy reflectance model suitable for both continuous and discontinuous canopies over sloping terrains (BOST). The scattering contributions of different canopies can be estimated by incorporating clumping indices; thus, we designed an algorithm to convert the clumping indices from flat terrain to sloping terrains, which explicitly considers the terrain effect on radiative transfer within canopies. In model validations, multiple schemes were used to obtain the objective evaluation results, including analyzing the terrain induced clumping index variations, comparing with the computer simulation model, typical canopy reflectance models, and the image reflectance in a real scene. The results suggested that the BOST model can capture the pattern of terrain-induced scattering components and reflectance distortions, and be successfully used in simulating the reflectance in a real mountainous region [RMSE (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) values of 0.0016 (0.889), 0.0183 (0.932), and 0.0023 (0.903) in the red, NIR, and green bands, respectively]. BOST performed better in terms of accuracy and efficiency than the typical models. Further testing of BOST in topographic normalization and biophysical parameter retrieval showed its practical usage potential. These validations confirmed the good performance of BOST in both continuous and discontinuous vegetation scenes and indicated it can provide a promising tool to retrieve biophysical parameters in mountainous areas.

Использование зимних и ранневесенних космоснимков для оценки проективного покрытия березняков на залежах

More than 40 million hectares of agricultural land were abandoned after the collapse of the Soviet Union. A significant part of the land is covered by spontaneously regenerating woody and shrubby vegetation. When identifying the forest regeneration, the stands with a tree cover of more than 50% are accurately identified. It is difficult to identify the initial stages of forest regeneration on the abandoned agricultural lands using summer satellite images because of little difference between the young trees and saplings due to their low height and low density on the one hand, and herbaceous vegetation on the other. The purpose of this work was to apply winter and early-spring satellite images for assessments of the tree cover of birch-dominated stands (Betula pen-dula Roth.) formed on the abandoned agricultural lands (See Fig. 1). We used 189 releves of birch forests on the abandoned agricultural lands in the broad-leaved forest zone of the Republic of Bashkortostan. A regression analysis of the evaluation of the tree cover was carried out using the values of the spectral reflectance of the RED, NIR, SWIR11, and SWIR12 bands, as well as the values of the NDFSI snow index from seven cloudless Sentinel-2 images taken between 04.11.2020 and 13.05.2021 (See Fig. 2, 3). When selecting optimal regression models, the values of correlation coefficients (R) and determination coefficients (R2) were used to assess the model quality. To test the possibility of using the obtained models for assessing the tree cover of the stand at earlier succession stages, we involved the data on the tree cover from 36 geobotanical releves, where the crown density of the stand was visually evaluated in July 2013. Then, the described procedure was applied to calculate the tree cover using the Landsat-8 image taken on 25.03.2014. When creating regression models to calculate the tree cover, the best results were obtained using the red band of early spring images during the period when snowpack is still solid (from mid-March to the first half of April) (See Table 1). The correlation between the tree cover and the spectral reflectance of the red band was -0.90. The model allowed us to determine accurately the tree cover of birch forests aged from 18 to 20 years which prevail in the zone of broad-leaved forests in the Republic of Bashkortostan. The accuracy of the model for determining the tree cover according to the obtained regression models for other dates is unstable and highly likely influenced by the snow depth and the seasonal dynamics of changes in the radiation intensity of the red and infrared bands (See Table 2, 3). To conclude, the equations calculated from modern satellite images can be used to assess the tree cover using retrospective images at earlier succession stages of the abandoned field recovery. When using early-spring images, the snow depth should be taken into account since the snowpack melting dates can vary greatly from year to year. The paper contains 3 Figures, 3 Tables, and 41 References. The Authors declare no conflict of interest.

Дистанційне визначення величини фотосинтетичного стоку органічного вуглецю (Ph) в агроценозі пшениці озимої залежно від строкатості ґрунтового покриву

The article presents the results of research on the relationship between soil differentiation and remote identification of the amount of photosynthetic runoff of organic carbon (Ph) in winter wheat crops. scientific work is devoted to the development of new and improvement of existing models of accumulation of biomass of agricultural crops, in order to form the structure of these models under different climatic conditions and on different types of soils. Models were developed during the research of carbon runoff values in the agrocenosis of winter wheat on sandy and light loam soils (PhСО2LIG and PhСLIG), medium and heavy loam soils (PhСО2HEAV and PhСHEAV) have been developed, and their accuracy has been assessed. Experimental studies have established and confirmed territorial consistency of the values of organic carbon runoff PhСО2LIG on wheat crops with geomorphological features of the quality of sandy and loamy soils, in particular the particle size distribution, gleying of soil and gley soil, was revealed. It is established that 95.2 % of the value of the error of remote identification of Ph of winter wheat biomass is due to the values of grain yield, straw and root mass. Taking into account the peculiarities of diagnostic phases of photosynthetic runoff of organic carbon phases of organogenesis of winter wheat for remote identification, the recommended average annual time windows of the Sentinel 2A image order in terms of natural and climatic zones of Ukraine have been developed. Thus, it is proposed to develop similar models for other satellites that produce images in the SWIR and NIR bands close to 2080-2350 nm and 1550-1750 nm, respectively. This will provide more images needed to diagnose carbon runoff in winter wheat crops.

Nitrogen Estimation for Wheat Using UAV-Based and Satellite Multispectral Imagery, Topographic Metrics, Leaf Area Index, Plant Height, Soil Moisture, and Machine Learning Methods

To improve productivity, reduce production costs, and minimize the environmental impacts of agriculture, the advancement of nitrogen (N) fertilizer management methods is needed. The objective of this study is to compare the use of Unmanned Aerial Vehicle (UAV) multispectral imagery and PlanetScope satellite imagery, together with plant height, leaf area index (LAI), soil moisture, and field topographic metrics to predict the canopy nitrogen weight (g/m2) of wheat fields in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models, applied to either UAV imagery or satellite imagery, were evaluated for canopy nitrogen weight prediction. The top-performing UAV imagery-based validation model used SVR with seven selected variables (plant height, LAI, four VIs, and the NIR band) with an R2 of 0.80 and an RMSE of 2.62 g/m2. The best satellite imagery-based validation model was RF, which used 17 variables including plant height, LAI, the four PlanetScope bands, and 11 VIs, resulting in an R2 of 0.92 and an RMSE of 1.75 g/m2. The model information can be used to improve field nitrogen predictions for the effective management of N fertilizer.

Two-step ResUp&Down generative adversarial network to reconstruct multispectral image from aerial RGB image

Convolutional neural network has brought breakthroughs on multispectral image reconstruction research. Previous work has largely focused on reconstructing MSI using the R-G-B channels from the MSI as inputs of the model. However, it’s image manipulation rather than practical use. In real application, to reconstruct multispectral image using images from RGB camera is a research that has hardly been studied. In this research, high resolution aerial RGB images are collected by drone with RGB camera and multispectral images are collected by drone with RedEdge-M multispectral Camera. Then a new two-step Generative Adversarial Network (GAN)-based reconstruction method was proposed as follows: At first, MSI and RGB images are carefully registered to make sure that pixels are one–one correspondent. Then two data sources are cropped to form dataset. After that, a novel R-MSI GAN using is proposed. It uses a ResUp&Down block to replace the ResNet block of the Generator network and it outperforms ResNet-based GAN. The experimental results show that: (1) the combination of Mean Square Error and Discriminator (MSE-D) can alleviate the problem of the high-frequency loss of generated images. (2) The root means square error (RMSE), mean relative absolute error (MRAE) and Structural Similarity (SSIM) can only reflect overall error but can’t reflect details in reconstructed images, while different bands' statistical histogram can present the total high-frequency loss of generated bands. (3) 3 indexes, which are intersection over union (IoU) based normalized difference vegetation index (NDVI)-IoU, normalized difference red edge (NDRE)-IoU and enhance vegetation index (EVI)-IoU, were defined to verify the effect of the generated MSI and they show good consistence with vegetation index map. 4 In comparisons among ResNet-based GAN, single step ResUp&Down GAN and two-step ResUp&Down GAN(T-GAN) with 3 loss functions (L1, MSE, Discriminator), the two-step ResUp&Down GAN(T-GAN) with MSE-D loss function performs best in reconstructing RGB bands. The T-GAN with L1loss-D (mean absolute error loss) performs best in reconstructing NIR and rededge bands. In summary, the proposed methods can effectively reconstruct MSI using images from RGB camera at drone based remote sensing.

Reducing Scaling Effect on Downscaled Land Surface Temperature Maps in Heterogenous Urban Environments

The literature review indicates that a scaling effect does exist in downscaling land surface temperature (DLST) processes, and no substantial methods were specially developed for addressing it. In this research, the main aim is to develop a new method to reduce the scaling effect on DLST maps at high resolutions. A thermal component-based thermal spectral unmixing (TSU) model was modified and a multiple regression (REG) model was adopted to create DLST maps at high resolutions. A combined variance of red and NIR bands at a very high resolution with a difference image between upscaled LST and DLST was used to develop a new method. With two case data sets, LSTs at coarse resolutions were downscaled by using the modified TSU model and the REG model to create DLST results. The new method with a correction term expression (a linear model created by using a semi-empirical approach) was used to improve the DLST maps in the two case study areas. The experimental results indicate that the new method could reduce the root mean square error and the mean absolute error &gt;30% and &gt;33%, respectively, and thus demonstrate that the proposed method was effective and significant, especially reducing the scaling effect on DLST results at very high resolutions. The novel significance for the new method is directly reducing the scaling effect on DLST maps at high resolutions.

Quantifying the poor purity and completeness of morphological samples selected by galaxy colour

ABSTRACT The galaxy population is strongly bimodal in both colour and morphology, and the two measures correlate strongly, with most blue galaxies being late-types (spirals) and most early-types, typically ellipticals, being red. This observation has led to the use of colour as a convenient selection criterion to make samples that are then labelled by morphology. Such use of colour as a proxy for morphology results in necessarily impure and incomplete samples. In this paper, we make use of the morphological labels produced by Galaxy Zoo to measure how incomplete and impure such samples are, considering optical (ugriz), near-ultraviolet (NUV), and near-infrared (NIR; JHK) bands. The best single colour optical selection is found using a threshold of g − r = 0.742, but this still results in a sample where only 56 per cent of red galaxies are smooth and 56 per cent of smooth galaxies are red. Use of the NUV gives some improvement over purely optical bands, particularly for late-types, but still results in low purity/completeness for early-types. No significant improvement is found by adding NIR bands. With any two bands, including NUV, a sample of early-types with greater than two-thirds purity cannot be constructed. Advances in quantitative galaxy morphologies have made colour–morphology proxy selections largely unnecessary going forward; where such assumptions are still required, we recommend studies carefully consider the implications of sample incompleteness/impurity.

A Comparison of Multi-Angle Implementation of Atmospheric Correction and MOD09 Daily Surface Reflectance Products From MODIS

This study presents the first systematic comparison of MAIAC Collection 6 MCD19A1 daily surface reflectance (SR) product with standard MODIS SR (MOD/MYD09). The study was limited to four tiles located in mid-Atlantic United States (H11V05), Canada (H12V03), central Amazon (H11V09), and North-Eastern China (H27V05) and used over 5000 MODIS granules in 2018. Overall, there is a remarkable agreement between the best quality pixels of the two products, in particular in the Red and NIR bands. Over selected tiles, the evaluation found that MAIAC provides from 4 to 25% more high-quality retrievals than MOD09 annually, with the largest difference in tropical regions, confirming results of the previous studies. The comparison of spectral characteristics showed a systematic MAIAC-MOD09 difference increasing from NIR to Blue, typical of biases of a Lambertian assumption in MOD09 algorithm. Over the North-Eastern China, MCD19A1 SR is found more stable at wide range of aerosol optical depth (AOD) variations, whereas MOD09 SR shows a consistent positive bias increasing with AOD and at shorter wavelengths. The observed SR differences can be attributed to differences in cloud detection, aerosol retrieval and in atmospheric correction which is performed using an accurate BRDF-coupled radiative transfer model in MAIAC and a Lambertian surface model in MOD09. While this study is not representative of the global performance because of its limited geographical coverage, it should help the land community to better understand the differences between the two products.

At-Sensor Radiometric Correction of a Multispectral Camera (RedEdge) for sUAS Vegetation Mapping.

Rapid advancement of drone technology enables small unmanned aircraft systems (sUAS) for quantitative applications in public and private sectors. The drone-mounted 5-band MicaSense RedEdge cameras, for example, have been popularly adopted in the agroindustry for assessment of crop healthiness. The camera extracts surface reflectance by referring to a pre-calibrated reflectance panel (CRP). This study tests the performance of a Matrace100/RedEdge-M camera in extracting surface reflectance orthoimages. Exploring multiple flights and field experiments, an at-sensor radiometric correction model was developed that integrated the default CRP and a Downwelling Light Sensor (DLS). Results at three vegetated sites reveal that the current CRP-only RedEdge-M correction procedure works fine except the NIR band, and the performance is less stable on cloudy days affected by sun diurnal, weather, and ground variations. The proposed radiometric correction model effectively reduces these local impacts to the extracted surface reflectance. Results also reveal that the Normalized Difference Vegetation Index (NDVI) from the RedEdge orthoimage is prone to overestimation and saturation in vegetated fields. Taking advantage of the camera’s red edge band centered at 717 nm, this study proposes a red edge NDVI (ReNDVI). The non-vegetation can be easily excluded with ReNDVI < 0.1. For vegetation, the ReNDVI provides reasonable values in a wider histogram than NDVI. It could be better applied to assess vegetation healthiness across the site.

A combined approach utilizing UAV 3D imaging methods, in-situ measurements, and laboratory experiments to assess water evaporation and trace element uptake by tree species growing in a red gypsum landfill

The extractive industry is increasingly faced with problems of managing contaminated sites. The red gypsum landfill at the Ochsenfeld site is representative of the typology byproduct storage of the Ti-extraction activity. The management of the elemental content and the water body are the issues at this site. The aim of this study was to evaluate the canopy conductance (gcmax) of various tree species and the content of elements in the leaves, utilizing the opportunity of a demonstration plantation setup in 2014 with sixteen tree species, combined with a growth chamber experiment. We combined the gas exchange measurements with the data from two multispectral cameras with RGB and NIR bands embarked on an unmanned aerial vehicle (UAV). In the field, Ostrya carpinifolia, Maclura pomifera, and Rhus copallina had the highest gcmax of all planted tree species, and the high transpiration rate in O. carpinifolia was confirmed in a pot-based controlled experiment. Except R. copallina, the species with a high Mn content (O. carpinifolia, Betula pendula, and Salix aquatica grandis) had high stomatal conductance. O. carpinifolia could therefore be a species to exploit in the management of landfill leachates, especially in the context of climate change since this species is well adapted to dry environments.

Digital mapping of potentially toxic elements enrichment in soils of Urmia Lake due to water level decline

Anthropogenic activities, in addition to climate change caused the drying of Urmia Lake in Iran, since 2005. Dust storms blown from the dried lakebed have created serious environmental hazards in adjacent areas. These crises would jeopardise achieving United Nations Sustainable Development Goals (UN SDGs) and emphasise the need for evaluating the spatial distribution of soil enrichment of potentially toxic elements (PTEs) (As, Cr, Cu, Ni, Pb and Zn). Conventional assessment would require a costly sampling method to map potentially polluted areas. Digital soil mapping (DSM) has proved to be a cost-efficient method for soil mapping, however its application in mapping enrichment of PTEs in soil is still lacking. This study aims to map and project the potential pollution of PTEs in the Urmia Lake area using digital mapping techniques and Landsat-8 OLI satellite images. A total of 129 surficial soil samples were collected as ground control. Enrichment factors (EFs) of PTEs and the Modified Pollution Index (MPI) were spatially predicted using two machine learning models. Covariates were derived from a suite of Landsat-8 spectral indices. The bootstrapping method was used to analyse the uncertainties. The results showed that Random Forests performed well in estimating EFs of several PTEs. Spectral indices using NIR and SWIR bands were key to predict these PTEs and MPI. The digital maps demonstrated that the study area was enriched with As, Cu and Pb at moderate to significant levels. Regions under the lower ecological level (elevation <-1274 m) had significantly larger enrichment than those of higher elevation. Based on MPI, 43% of the area was categorised as moderately polluted, and 31% of the area was moderately-heavily polluted. Possible sources of PTEs were discharges from farmlands, landfills, and industries. Our results revealed that the Urmia Lake desiccating has caused severe environmental challenges and needs immediate restoration.